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A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology

Indica Labs / HALO AI Publications  / A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology

A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology

Martin, D.R., Hanson, J.A., Gullapalli, R.R., Schultz, F.A., Sethi, A. and Clark, D.P.

Archives of Pathology & Laboratory Medicine In-Press | Published April 5, 2019 | DOI: 10.5858/arpa.2019-0004-OA

Context.— Most deep learning (DL) studies have focused on neoplastic pathology, with the realm of inflammatory pathology remaining largely untouched.

Objective.— To investigate the use of DL for nonneoplastic gastric biopsies.

Design.— Gold standard diagnoses were blindly established by 2 gastrointestinal pathologists. For phase 1, 300 classic cases (100 normal, 100 Helicobacter pylori, 100 reactive gastropathy) that best displayed the desired pathology were scanned and annotated for DL analysis. A total of 70% of the cases for each group were selected for the training set, and 30% were included in the test set. The software assigned colored labels to the test biopsies, which corresponded to the area of the tissue assigned a diagnosis by the DL algorithm, termed area distribution (AD). For Phase 2, an additional 106 consecutive nonclassical gastric biopsies from our archives were tested in the same fashion.

Results.— For Phase 1, receiver operating curves showed near perfect agreement with the gold standard diagnoses at an AD percentage cutoff of 50% for normal (area under the curve [AUC] = 99.7%) and H pylori (AUC = 100%), and 40% for reactive gastropathy (AUC = 99.9%). Sensitivity/specificity pairings were as follows: normal (96.7%, 86.7%), H pylori (100%, 98.3%), and reactive gastropathy (96.7%, 96.7%). For phase 2, receiver operating curves were slightly less discriminatory, with optimal AD cutoffs reduced to 40% across diagnostic groups. The AUCs were 91.9% for normal, 100% for H pylori, and 94.0% for reactive gastropathy. Sensitivity/specificity parings were as follows: normal (73.7%, 79.6%), H pylori (95.7%, 100%), reactive gastropathy (100%, 62.5%).

Conclusions.— A convolutional neural network can serve as an effective screening tool/diagnostic aid for H pylori gastritis.

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A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology

Indica Labs / HALO AI Publications  / A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology

A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology

Martin, D.R., Hanson, J.A., Gullapalli, R.R., Schultz, F.A., Sethi, A. and Clark, D.P.

Archives of Pathology & Laboratory Medicine In-Press | Published April 5, 2019 | DOI: 10.5858/arpa.2019-0004-OA

Context.— Most deep learning (DL) studies have focused on neoplastic pathology, with the realm of inflammatory pathology remaining largely untouched.

Objective.— To investigate the use of DL for nonneoplastic gastric biopsies.

Design.— Gold standard diagnoses were blindly established by 2 gastrointestinal pathologists. For phase 1, 300 classic cases (100 normal, 100 Helicobacter pylori, 100 reactive gastropathy) that best displayed the desired pathology were scanned and annotated for DL analysis. A total of 70% of the cases for each group were selected for the training set, and 30% were included in the test set. The software assigned colored labels to the test biopsies, which corresponded to the area of the tissue assigned a diagnosis by the DL algorithm, termed area distribution (AD). For Phase 2, an additional 106 consecutive nonclassical gastric biopsies from our archives were tested in the same fashion.

Results.— For Phase 1, receiver operating curves showed near perfect agreement with the gold standard diagnoses at an AD percentage cutoff of 50% for normal (area under the curve [AUC] = 99.7%) and H pylori (AUC = 100%), and 40% for reactive gastropathy (AUC = 99.9%). Sensitivity/specificity pairings were as follows: normal (96.7%, 86.7%), H pylori (100%, 98.3%), and reactive gastropathy (96.7%, 96.7%). For phase 2, receiver operating curves were slightly less discriminatory, with optimal AD cutoffs reduced to 40% across diagnostic groups. The AUCs were 91.9% for normal, 100% for H pylori, and 94.0% for reactive gastropathy. Sensitivity/specificity parings were as follows: normal (73.7%, 79.6%), H pylori (95.7%, 100%), reactive gastropathy (100%, 62.5%).

Conclusions.— A convolutional neural network can serve as an effective screening tool/diagnostic aid for H pylori gastritis.

Click here to access full article.